text: from collections import OrderedDict
  import logging
  import numpy as np
  import gym
  from typing import Any, List
  
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.spaces.repeated import Repeated
  from ray.rllib.utils.typing import TensorType
  from ray.rllib.utils.images import resize
  
  ATARI_OBS_SHAPE = (210, 160, 3)
  ATARI_RAM_OBS_SHAPE = (128, )
  
  # Only validate env observations vs the observation space every n times in a
  # Preprocessor.
  OBS_VALIDATION_INTERVAL = 100
  
  logger = logging.getLogger(__name__)
  
  
  @PublicAPI
  class Preprocessor:
      """Defines an abstract observation preprocessor function.
  
      Attributes:
          shape (List[int]): Shape of the preprocessed output.
      """
  
      @PublicAPI
      def __init__(self, obs_space: gym.Space, options: dict = None):
          legacy_patch_shapes(obs_space)
          self._obs_space = obs_space
          if not options:
              from ray.rllib.models.catalog import MODEL_DEFAULTS
              self._options = MODEL_DEFAULTS.copy()
          else:
              self._options = options
          self.shape = self._init_shape(obs_space, self._options)
          self._size = int(np.product(self.shape))
          self._i = 0
  
      @PublicAPI
      def _init_shape(self, obs_space: gym.Space, options: dict) -> List[int]:
          """Returns the shape after preprocessing."""
          raise NotImplementedError
  
      @PublicAPI
      def transform(self, observation: TensorType) -> np.ndarray:
          """Returns the preprocessed observation."""
          raise NotImplementedError
  
      def write(self, observation: TensorType, array: np.ndarray,
                offset: int) -> None:
          """Alternative to transform for more efficient flattening."""
          array[offset:offset + self._size] = self.transform(observation)
  
      def check_shape(self, observation: Any) -> None:
          """Checks the shape of the given observation."""
          if self._i % OBS_VALIDATION_INTERVAL == 0:
              if type(observation) is list and isinstance(
                      self._obs_space, gym.spaces.Box):
                  observation = np.array(observation)
              try:
                  if not self._obs_space.contains(observation):
                      raise ValueError(
                          "Observation ({}) outside given space ({})!",
                          observation, self._obs_space)
              except AttributeError:
                  raise ValueError(
                      "Observation for a Box/MultiBinary/MultiDiscrete space "
                      "should be an np.array, not a Python list.", observation)
          self._i += 1
  
      @property
      @PublicAPI
      def size(self) -> int:
          return self._size
  
      @property
      @PublicAPI
      def observation_space(self) -> gym.Space:
          obs_space = gym.spaces.Box(-1., 1., self.shape, dtype=np.float32)
          # Stash the unwrapped space so that we can unwrap dict and tuple spaces
          # automatically in modelv2.py
          classes = (DictFlatteningPreprocessor, OneHotPreprocessor,
                     RepeatedValuesPreprocessor, TupleFlatteningPreprocessor)
          if isinstance(self, classes):
              obs_space.original_space = self._obs_space
          return obs_space
  
  
  class GenericPixelPreprocessor(Preprocessor):
      """Generic image preprocessor.
  
      Note: for Atari games, use config {"preprocessor_pref": "deepmind"}
      instead for deepmind-style Atari preprocessing.
      """
  
      @override(Preprocessor)
      def _init_shape(self, obs_space: gym.Space, options: dict) -> List[int]:
          self._grayscale = options.get("grayscale")
          self._zero_mean = options.get("zero_mean")
          self._dim = options.get("dim")
          if self._grayscale:
              shape = (self._dim, self._dim, 1)
          else:
              shape = (self._dim, self._dim, 3)
  
          return shape
  
      @override(Preprocessor)
      def transform(self, observation: TensorType) -> np.ndarray:
          """Downsamples images from (210, 160, 3) by the configured factor."""
          self.check_shape(observation)
          scaled = observation[25:-25, :, :]
          if self._dim < 84:
              scaled = resize(scaled, height=84, width=84)
          # OpenAI: Resize by half, then down to 42x42 (essentially mipmapping).
          # If we resize directly we lose pixels that, when mapped to 42x42,
          # aren't close enough to the pixel boundary.
          scaled = resize(scaled, height=self._dim, width=self._dim)
          if self._grayscale:
              scaled = scaled.mean(2)
              scaled = scaled.astype(np.float32)
              # Rescale needed for maintaining 1 channel
              scaled = np.reshape(scaled, [self._dim, self._dim, 1])
          if self._zero_mean:
              scaled = (scaled - 128) / 128
          else:
              scaled *= 1.0 / 255.0
          return scaled
  
  
  class AtariRamPreprocessor(Preprocessor):
      @override(Preprocessor)
      def _init_shape(self, obs_space: gym.Space, options: dict) -> List[int]:
          return (128, )
  
      @override(Preprocessor)
      def transform(self, observation: TensorType) -> np.ndarray:
          self.check_shape(observation)
          return (observation.astype("float32") - 128) / 128
  
  
  class OneHotPreprocessor(Preprocessor):
      """One-hot preprocessor for Discrete and MultiDiscrete spaces.
  
      Examples:
          >>> self.transform(Discrete(3).sample())
          ... np.array([0.0, 1.0, 0.0])
          >>> self.transform(MultiDiscrete([2, 3]).sample())
          ... np.array([0.0, 1.0, 0.0, 0.0, 1.0])
      """
  
      @override(Preprocessor)
      def _init_shape(self, obs_space: gym.Space, options: dict) -> List[int]:
          if isinstance(obs_space, gym.spaces.Discrete):
              return (self._obs_space.n, )
          else:
              return (np.sum(self._obs_space.nvec), )
  
      @override(Preprocessor)
      def transform(self, observation: TensorType) -> np.ndarray:
          self.check_shape(observation)
          arr = np.zeros(self._init_shape(self._obs_space, {}), dtype=np.float32)
          if isinstance(self._obs_space, gym.spaces.Discrete):
              arr[observation] = 1
          else:
              for i, o in enumerate(observation):
                  arr[np.sum(self._obs_space.nvec[:i]) + o] = 1
          return arr
  
      @override(Preprocessor)
      def write(self, observation: TensorType, array: np.ndarray,
                offset: int) -> None:
          array[offset:offset + self.size] = self.transform(observation)
  
  
  class NoPreprocessor(Preprocessor):
      @override(Preprocessor)
      def _init_shape(self, obs_space: gym.Space, options: dict) -> List[int]:
          return self._obs_space.shape
  
      @override(Preprocessor)
      def transform(self, observation: TensorType) -> np.ndarray:
          self.check_shape(observation)
          return observation
  
      @override(Preprocessor)
      def write(self, observation: TensorType, array: np.ndarray,
                offset: int) -> None:
          array[offset:offset + self._size] = np.array(
              observation, copy=False).ravel()
  
      @property
      @override(Preprocessor)
      def observation_space(self) -> gym.Space:
          return self._obs_space
  
  
  class TupleFlatteningPreprocessor(Preprocessor):
      """Preprocesses each tuple element, then flattens it all into a vector.
  
      RLlib models will unpack the flattened output before _build_layers_v2().
      """
  
      @override(Preprocessor)
      def _init_shape(self, obs_space: gym.Space, options: dict) -> List[int]:
          assert isinstance(self._obs_space, gym.spaces.Tuple)
          size = 0
          self.preprocessors = []
          for i in range(len(self._obs_space.spaces)):
              space = self._obs_space.spaces[i]
              logger.debug("Creating sub-preprocessor for {}".format(space))
              preprocessor = get_preprocessor(space)(space, self._options)
              self.preprocessors.append(preprocessor)
              size += preprocessor.size
          return (size, )
  
      @override(Preprocessor)
      def transform(self, observation: TensorType) -> np.ndarray:
          self.check_shape(observation)
          array = np.zeros(self.shape, dtype=np.float32)
          self.write(observation, array, 0)
          return array
  
      @override(Preprocessor)
      def write(self, observation: TensorType, array: np.ndarray,
                offset: int) -> None:
          assert len(observation) == len(self.preprocessors), observation
          for o, p in zip(observation, self.preprocessors):
              p.write(o, array, offset)
              offset += p.size
  
  
  class DictFlatteningPreprocessor(Preprocessor):
      """Preprocesses each dict value, then flattens it all into a vector.
  
      RLlib models will unpack the flattened output before _build_layers_v2().
      """
  
      @override(Preprocessor)
      def _init_shape(self, obs_space: gym.Space, options: dict) -> List[int]:
          assert isinstance(self._obs_space, gym.spaces.Dict)
          size = 0
          self.preprocessors = []
          for space in self._obs_space.spaces.values():
              logger.debug("Creating sub-preprocessor for {}".format(space))
              preprocessor = get_preprocessor(space)(space, self._options)
              self.preprocessors.append(preprocessor)
              size += preprocessor.size
          return (size, )
  
      @override(Preprocessor)
      def transform(self, observation: TensorType) -> np.ndarray:
          self.check_shape(observation)
          array = np.zeros(self.shape, dtype=np.float32)
          self.write(observation, array, 0)
          return array
  
      @override(Preprocessor)
      def write(self, observation: TensorType, array: np.ndarray,
                offset: int) -> None:
          if not isinstance(observation, OrderedDict):
              observation = OrderedDict(sorted(observation.items()))
          assert len(observation) == len(self.preprocessors), \
              (len(observation), len(self.preprocessors))
          for o, p in zip(observation.values(), self.preprocessors):
              p.write(o, array, offset)
              offset += p.size
  
  
  class RepeatedValuesPreprocessor(Preprocessor):
      """Pads and batches the variable-length list value."""
  
      @override(Preprocessor)
      def _init_shape(self, obs_space: gym.Space, options: dict) -> List[int]:
          assert isinstance(self._obs_space, Repeated)
          child_space = obs_space.child_space
          self.child_preprocessor = get_preprocessor(child_space)(child_space,
                                                                  self._options)
          # The first slot encodes the list length.
          size = 1 + self.child_preprocessor.size * obs_space.max_len
          return (size, )
  
      @override(Preprocessor)
      def transform(self, observation: TensorType) -> np.ndarray:
          array = np.zeros(self.shape)
          if isinstance(observation, list):
              for elem in observation:
                  self.child_preprocessor.check_shape(elem)
          else:
              pass  # ValueError will be raised in write() below.
          self.write(observation, array, 0)
          return array
  
      @override(Preprocessor)
      def write(self, observation: TensorType, array: np.ndarray,
                offset: int) -> None:
          if not isinstance(observation, list):
              raise ValueError("Input for {} must be list type, got {}".format(
                  self, observation))
          elif len(observation) > self._obs_space.max_len:
              raise ValueError("Input {} exceeds max len of space {}".format(
                  observation, self._obs_space.max_len))
          # The first slot encodes the list length.
          array[offset] = len(observation)
          for i, elem in enumerate(observation):
              offset_i = offset + 1 + i * self.child_preprocessor.size
              self.child_preprocessor.write(elem, array, offset_i)
  
  
  @PublicAPI
  def get_preprocessor(space: gym.Space) -> type:
      """Returns an appropriate preprocessor class for the given space."""
  
      legacy_patch_shapes(space)
      obs_shape = space.shape
  
      if isinstance(space, (gym.spaces.Discrete, gym.spaces.MultiDiscrete)):
          preprocessor = OneHotPreprocessor
      elif obs_shape == ATARI_OBS_SHAPE:
          preprocessor = GenericPixelPreprocessor
      elif obs_shape == ATARI_RAM_OBS_SHAPE:
          preprocessor = AtariRamPreprocessor
      elif isinstance(space, gym.spaces.Tuple):
          preprocessor = TupleFlatteningPreprocessor
      elif isinstance(space, gym.spaces.Dict):
          preprocessor = DictFlatteningPreprocessor
      elif isinstance(space, Repeated):
          preprocessor = RepeatedValuesPreprocessor
      else:
          preprocessor = NoPreprocessor
  
      return preprocessor
  
  
  def legacy_patch_shapes(space: gym.Space) -> List[int]:
      """Assigns shapes to spaces that don't have shapes.
  
      This is only needed for older gym versions that don't set shapes properly
      for Tuple and Discrete spaces.
      """
  
      if not hasattr(space, "shape"):
          if isinstance(space, gym.spaces.Discrete):
              space.shape = ()
          elif isinstance(space, gym.spaces.Tuple):
              shapes = []
              for s in space.spaces:
                  shape = legacy_patch_shapes(s)
                  shapes.append(shape)
              space.shape = tuple(shapes)
  
      return space.shape
  